Overview

Dataset statistics

Number of variables13
Number of observations810
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory82.4 KiB
Average record size in memory104.2 B

Variable types

Numeric12
DateTime1

Alerts

unpaid has constant value "2363179471491548.0" Constant
df_index is highly correlated with relative_hourHigh correlation
memory_usage is highly correlated with cpu_temp and 4 other fieldsHigh correlation
cpu_temp is highly correlated with memory_usage and 3 other fieldsHigh correlation
gpu_memory_usage is highly correlated with memory_usage and 3 other fieldsHigh correlation
gpu_load is highly correlated with memory_usage and 4 other fieldsHigh correlation
gpu_temp is highly correlated with memory_usage and 4 other fieldsHigh correlation
reported_hashrate is highly correlated with memory_usage and 4 other fieldsHigh correlation
relative_hour is highly correlated with df_indexHigh correlation
df_index is uniformly distributed Uniform
relative_hour is uniformly distributed Uniform
df_index has unique values Unique
ts has unique values Unique
relative_hour has unique values Unique
gpu_load has 232 (28.6%) zeros Zeros
reported_hashrate has 232 (28.6%) zeros Zeros

Reproduction

Analysis started2021-12-01 03:33:19.492676
Analysis finished2021-12-01 03:33:38.410795
Duration18.92 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct810
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean698.5
Minimum294
Maximum1103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:38.482152image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum294
5-th percentile334.45
Q1496.25
median698.5
Q3900.75
95-th percentile1062.55
Maximum1103
Range809
Interquartile range (IQR)404.5

Descriptive statistics

Standard deviation233.9711521
Coefficient of variation (CV)0.3349622793
Kurtosis-1.2
Mean698.5
Median Absolute Deviation (MAD)202.5
Skewness0
Sum565785
Variance54742.5
MonotonicityStrictly increasing
2021-11-30T22:33:38.629062image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2941
 
0.1%
8371
 
0.1%
8271
 
0.1%
8281
 
0.1%
8291
 
0.1%
8301
 
0.1%
8311
 
0.1%
8321
 
0.1%
8331
 
0.1%
8341
 
0.1%
Other values (800)800
98.8%
ValueCountFrequency (%)
2941
0.1%
2951
0.1%
2961
0.1%
2971
0.1%
2981
0.1%
ValueCountFrequency (%)
11031
0.1%
11021
0.1%
11011
0.1%
11001
0.1%
10991
0.1%

ts
Date

UNIQUE

Distinct810
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Minimum2021-11-05 14:31:06-05:00
Maximum2021-11-05 16:51:07-05:00
2021-11-30T22:33:38.852103image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:38.979501image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cpu_load
Real number (ℝ≥0)

Distinct12
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2237037037
Minimum0.1
Maximum1.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:39.091318image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.2
Q30.3
95-th percentile0.4
Maximum1.8
Range1.7
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1518932392
Coefficient of variation (CV)0.6789929565
Kurtosis29.47512796
Mean0.2237037037
Median Absolute Deviation (MAD)0.1
Skewness4.098883739
Sum181.2
Variance0.0230715561
MonotonicityNot monotonic
2021-11-30T22:33:39.180698image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.2295
36.4%
0.1252
31.1%
0.3207
25.6%
0.416
 
2.0%
0.512
 
1.5%
0.710
 
1.2%
0.69
 
1.1%
0.84
 
0.5%
1.52
 
0.2%
1.21
 
0.1%
Other values (2)2
 
0.2%
ValueCountFrequency (%)
0.1252
31.1%
0.2295
36.4%
0.3207
25.6%
0.416
 
2.0%
0.512
 
1.5%
ValueCountFrequency (%)
1.81
 
0.1%
1.52
0.2%
1.21
 
0.1%
0.91
 
0.1%
0.84
0.5%

cpu_freq
Real number (ℝ≥0)

Distinct790
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean927.9748395
Minimum808.29
Maximum3593.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:39.298315image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum808.29
5-th percentile816.498
Q1834.765
median861.065
Q3911.1125
95-th percentile1191.525
Maximum3593.75
Range2785.46
Interquartile range (IQR)76.3475

Descriptive statistics

Standard deviation275.8411266
Coefficient of variation (CV)0.2972506525
Kurtosis52.41213246
Mean927.9748395
Median Absolute Deviation (MAD)32.42
Skewness6.64451306
Sum751659.62
Variance76088.32712
MonotonicityNot monotonic
2021-11-30T22:33:39.434164image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
887.663
 
0.4%
910.942
 
0.2%
1097.962
 
0.2%
873.012
 
0.2%
853.712
 
0.2%
10982
 
0.2%
854.952
 
0.2%
851.412
 
0.2%
892.122
 
0.2%
846.572
 
0.2%
Other values (780)789
97.4%
ValueCountFrequency (%)
808.291
0.1%
808.531
0.1%
808.581
0.1%
808.641
0.1%
808.781
0.1%
ValueCountFrequency (%)
3593.751
0.1%
3593.211
0.1%
3593.121
0.1%
3592.751
0.1%
3006.561
0.1%

memory_usage
Real number (ℝ≥0)

HIGH CORRELATION

Distinct680
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1340648693
Minimum1235804160
Maximum1387003904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:39.643581image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1235804160
5-th percentile1237534720
Q11238943744
median1380288512
Q31383370752
95-th percentile1385527501
Maximum1387003904
Range151199744
Interquartile range (IQR)144427008

Descriptive statistics

Standard deviation65203082.51
Coefficient of variation (CV)0.0486354724
Kurtosis-1.120239848
Mean1340648693
Median Absolute Deviation (MAD)3704832
Skewness-0.9367020791
Sum1.085925442 × 1012
Variance4.251441969 × 1015
MonotonicityNot monotonic
2021-11-30T22:33:39.790919image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12379176966
 
0.7%
12384337925
 
0.6%
13801103364
 
0.5%
12388884484
 
0.5%
13852098563
 
0.4%
12394741763
 
0.4%
12381511683
 
0.4%
12386836483
 
0.4%
12374261763
 
0.4%
13843292163
 
0.4%
Other values (670)773
95.4%
ValueCountFrequency (%)
12358041602
0.2%
12362792961
0.1%
12363038721
0.1%
12363202562
0.2%
12363284481
0.1%
ValueCountFrequency (%)
13870039041
0.1%
13867294721
0.1%
13867212801
0.1%
13865656321
0.1%
13863608321
0.1%

cpu_temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.00617284
Minimum26
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:39.953212image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile27
Q131
median33
Q334
95-th percentile34
Maximum35
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.369712352
Coefficient of variation (CV)0.07403922875
Kurtosis-0.4589432815
Mean32.00617284
Median Absolute Deviation (MAD)1
Skewness-1.006907634
Sum25925
Variance5.615536633
MonotonicityNot monotonic
2021-11-30T22:33:40.042364image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
33286
35.3%
34218
26.9%
2897
 
12.0%
3265
 
8.0%
2747
 
5.8%
2935
 
4.3%
3524
 
3.0%
3119
 
2.3%
3018
 
2.2%
261
 
0.1%
ValueCountFrequency (%)
261
 
0.1%
2747
5.8%
2897
12.0%
2935
 
4.3%
3018
 
2.2%
ValueCountFrequency (%)
3524
 
3.0%
34218
26.9%
33286
35.3%
3265
 
8.0%
3119
 
2.3%

gpu_memory_usage
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2177081971
Minimum9437184
Maximum3052404736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:40.133090image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9437184
5-th percentile9437184
Q19437184
median3052404736
Q33052404736
95-th percentile3052404736
Maximum3052404736
Range3042967552
Interquartile range (IQR)3042967552

Descriptive statistics

Standard deviation1378308449
Coefficient of variation (CV)0.6330990137
Kurtosis-1.119291326
Mean2177081971
Median Absolute Deviation (MAD)0
Skewness-0.9399353536
Sum1.763436397 × 1012
Variance1.899734179 × 1018
MonotonicityDecreasing
2021-11-30T22:33:40.213521image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
3052404736577
71.2%
9437184233
28.8%
ValueCountFrequency (%)
9437184233
28.8%
3052404736577
71.2%
ValueCountFrequency (%)
3052404736577
71.2%
9437184233
28.8%

gpu_load
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.34567901
Minimum0
Maximum100
Zeros232
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:40.300364image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation45.23026118
Coefficient of variation (CV)0.6339593624
Kurtosis-1.106772296
Mean71.34567901
Median Absolute Deviation (MAD)0
Skewness-0.9464282946
Sum57790
Variance2045.776526
MonotonicityDecreasing
2021-11-30T22:33:40.381053image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
100577
71.2%
0232
28.6%
901
 
0.1%
ValueCountFrequency (%)
0232
28.6%
901
 
0.1%
100577
71.2%
ValueCountFrequency (%)
100577
71.2%
901
 
0.1%
0232
28.6%

gpu_temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.14691358
Minimum30
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:40.478961image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q137.25
median65
Q366
95-th percentile66
Maximum66
Range36
Interquartile range (IQR)28.75

Descriptive statistics

Standard deviation14.82670882
Coefficient of variation (CV)0.2640698816
Kurtosis-0.8937938009
Mean56.14691358
Median Absolute Deviation (MAD)1
Skewness-1.024816381
Sum45479
Variance219.8312945
MonotonicityNot monotonic
2021-11-30T22:33:40.587731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
65367
45.3%
66210
25.9%
3199
 
12.2%
3046
 
5.7%
3222
 
2.7%
3313
 
1.6%
348
 
1.0%
356
 
0.7%
366
 
0.7%
394
 
0.5%
Other values (20)29
 
3.6%
ValueCountFrequency (%)
3046
5.7%
3199
12.2%
3222
 
2.7%
3313
 
1.6%
348
 
1.0%
ValueCountFrequency (%)
66210
25.9%
65367
45.3%
641
 
0.1%
601
 
0.1%
581
 
0.1%

hashrate
Real number (ℝ≥0)

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2386831.273
Minimum1111111.11
Maximum3333333.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:40.691656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1111111.11
5-th percentile1111111.11
Q12222222.22
median2222222.22
Q33333333.33
95-th percentile3333333.33
Maximum3333333.33
Range2222222.22
Interquartile range (IQR)1111111.11

Descriptive statistics

Standard deviation709882.3481
Coefficient of variation (CV)0.2974162254
Kurtosis-0.6079048716
Mean2386831.273
Median Absolute Deviation (MAD)0
Skewness-0.1396926795
Sum1933333331
Variance5.039329481 × 1011
MonotonicityNot monotonic
2021-11-30T22:33:40.851419image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2222222.22462
57.0%
3333333.33234
28.9%
1111111.11114
 
14.1%
ValueCountFrequency (%)
1111111.11114
 
14.1%
2222222.22462
57.0%
3333333.33234
28.9%
ValueCountFrequency (%)
3333333.33234
28.9%
2222222.22462
57.0%
1111111.11114
 
14.1%

unpaid
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.363179471 × 1015
Minimum2.363179471 × 1015
Maximum2.363179471 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:40.950986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2.363179471 × 1015
5-th percentile2.363179471 × 1015
Q12.363179471 × 1015
median2.363179471 × 1015
Q32.363179471 × 1015
95-th percentile2.363179471 × 1015
Maximum2.363179471 × 1015
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean2.363179471 × 1015
Median Absolute Deviation (MAD)0
Skewness0
Sum1.914175372 × 1018
Variance0
MonotonicityIncreasing
2021-11-30T22:33:41.073555image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
2.363179471 × 1015810
100.0%
ValueCountFrequency (%)
2.363179471 × 1015810
100.0%
ValueCountFrequency (%)
2.363179471 × 1015810
100.0%

reported_hashrate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean977435.8025
Minimum0
Maximum1370000
Zeros232
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:41.164846image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1370000
Q31370000
95-th percentile1370000
Maximum1370000
Range1370000
Interquartile range (IQR)1370000

Descriptive statistics

Standard deviation619654.5782
Coefficient of variation (CV)0.6339593624
Kurtosis-1.106772296
Mean977435.8025
Median Absolute Deviation (MAD)0
Skewness-0.9464282946
Sum791723000
Variance3.839717962 × 1011
MonotonicityDecreasing
2021-11-30T22:33:41.250723image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1370000577
71.2%
0232
28.6%
12330001
 
0.1%
ValueCountFrequency (%)
0232
28.6%
12330001
 
0.1%
1370000577
71.2%
ValueCountFrequency (%)
1370000577
71.2%
12330001
 
0.1%
0232
28.6%

relative_hour
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct810
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.013769547
Minimum0.8469444444
Maximum3.180555556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2021-11-30T22:33:41.367071image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.8469444444
5-th percentile0.9635972222
Q11.430694444
median2.013888889
Q32.597013889
95-th percentile3.063625
Maximum3.180555556
Range2.333611111
Interquartile range (IQR)1.166319444

Descriptive statistics

Standard deviation0.6747170374
Coefficient of variation (CV)0.3350517632
Kurtosis-1.199311354
Mean2.013769547
Median Absolute Deviation (MAD)0.5838888889
Skewness-0.0001749758104
Sum1631.153333
Variance0.4552430806
MonotonicityStrictly increasing
2021-11-30T22:33:41.505896image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.84694444441
 
0.1%
2.4130555561
 
0.1%
2.3841666671
 
0.1%
2.3869444441
 
0.1%
2.391
 
0.1%
2.3927777781
 
0.1%
2.3958333331
 
0.1%
2.3986111111
 
0.1%
2.4013888891
 
0.1%
2.4044444441
 
0.1%
Other values (800)800
98.8%
ValueCountFrequency (%)
0.84694444441
0.1%
0.851
0.1%
0.85277777781
0.1%
0.85555555561
0.1%
0.85861111111
0.1%
ValueCountFrequency (%)
3.1805555561
0.1%
3.17751
0.1%
3.1747222221
0.1%
3.1719444441
0.1%
3.1688888891
0.1%

Interactions

2021-11-30T22:33:36.553555image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:19.799809image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:21.288016image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:23.035792image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:24.761112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.450266image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.751789image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.217150image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:30.582461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:32.098232image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:33.576393image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.942081image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:36.707807image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:19.958340image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:21.414397image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:23.274325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:24.896094image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.567008image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.881511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.334499image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:30.709913image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:32.224045image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:33.694135image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:35.073247image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:36.819242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:20.078830image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:21.538619image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:23.494565image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:25.056675image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.678421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.999452image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.446426image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:30.909656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:32.342265image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:33.804348image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:35.200504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:36.926789image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:20.192835image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:21.672191image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:23.628708image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:25.203307image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.783514image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:28.111808image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.556731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.024466image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:32.469394image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:33.911083image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:35.338797image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:37.043196image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:20.317691image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:21.804286image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:23.765907image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:25.342724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.897492image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:28.311241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.674695image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.152561image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:32.600083image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.029483image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:35.462419image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:37.157602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:20.430429image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:21.917917image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:23.891347image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:25.474476image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.998715image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:28.418582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.779970image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.264992image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:32.706513image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.133363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:35.575409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:37.301788image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:20.564766image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:22.057080image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:24.014189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:25.693992image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.109545image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:28.533731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.893307image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.380737image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:32.820104image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.245773image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:35.727779image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:37.409865image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:20.680802image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:22.188479image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:24.151830image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:25.821531image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.214296image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:28.644761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.999446image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.493186image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:32.933149image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.352134image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:35.855454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:37.526502image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:20.800505image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:22.418546image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:24.292590image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:25.950835image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.325800image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:28.763640image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:30.116238image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.625688image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:33.053925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.467130image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:35.978693image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:37.630218image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:20.926114image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:22.531003image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:24.403219image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.072441image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.429670image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:28.874384image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:30.243924image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.736678image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:33.165170image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.573918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:36.094537image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:37.754031image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:21.048908image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:22.641524image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:24.521116image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.198114image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.533517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:28.986135image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:30.351573image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.846256image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:33.272004image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.680668image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:36.307168image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:37.874651image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:21.175288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:22.846808image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:24.649856image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:26.333774image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:27.647962image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:29.107161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:30.468486image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:31.968830image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:33.389683image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:34.827259image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:36.436527image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2021-11-30T22:33:41.628363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-30T22:33:41.818317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-30T22:33:42.004978image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-30T22:33:42.194289image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-30T22:33:38.080021image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-30T22:33:38.329901image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
02942021-11-05 14:31:06-05:000.2958.45138209280033.03.052405e+09100.065.02222222.222.363179e+151370000.00.846944
12952021-11-05 14:31:17-05:000.2903.91138196582435.03.052405e+09100.066.02222222.222.363179e+151370000.00.850000
22962021-11-05 14:31:27-05:000.2819.56138117529634.03.052405e+09100.065.02222222.222.363179e+151370000.00.852778
32972021-11-05 14:31:37-05:000.2960.24138264166434.03.052405e+09100.066.02222222.222.363179e+151370000.00.855556
42982021-11-05 14:31:48-05:000.1895.10138282188834.03.052405e+09100.066.02222222.222.363179e+151370000.00.858611
52992021-11-05 14:31:58-05:000.3827.88138285056033.03.052405e+09100.065.02222222.222.363179e+151370000.00.861389
63002021-11-05 14:32:08-05:000.2946.81138237542434.03.052405e+09100.066.02222222.222.363179e+151370000.00.864167
73012021-11-05 14:32:19-05:000.31297.41138236313633.03.052405e+09100.066.02222222.222.363179e+151370000.00.867222
83022021-11-05 14:32:29-05:000.2931.95138286284833.03.052405e+09100.066.02222222.222.363179e+151370000.00.870000
93032021-11-05 14:32:39-05:000.3830.37138207232033.03.052405e+09100.066.02222222.222.363179e+151370000.00.872778

Last rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
80010942021-11-05 16:49:33-05:000.1815.68123815116827.09437184.00.030.01111111.112.363179e+150.03.154444
80110952021-11-05 16:49:44-05:000.1808.29123815116827.09437184.00.030.01111111.112.363179e+150.03.157500
80210962021-11-05 16:49:54-05:000.1828.76123788492828.09437184.00.030.01111111.112.363179e+150.03.160278
80310972021-11-05 16:50:04-05:000.1845.28123791769628.09437184.00.031.01111111.112.363179e+150.03.163056
80410982021-11-05 16:50:15-05:000.1921.35123791769628.09437184.00.030.01111111.112.363179e+150.03.166111
80510992021-11-05 16:50:25-05:000.1861.53123791769628.09437184.00.030.01111111.112.363179e+150.03.168889
80611002021-11-05 16:50:36-05:000.1830.31123790950427.09437184.00.030.01111111.112.363179e+150.03.171944
80711012021-11-05 16:50:46-05:000.51098.15123739340827.09437184.00.031.01111111.112.363179e+150.03.174722
80811022021-11-05 16:50:56-05:000.5824.40123713536027.09437184.00.031.01111111.112.363179e+150.03.177500
80911032021-11-05 16:51:07-05:000.1814.65123737702427.09437184.00.030.01111111.112.363179e+150.03.180556